Trajectory set empowered hypergraph transformer for mobile sensor based traffic prediction
Publication Type
Conference Proceeding Article
Publication Date
4-2024
Abstract
Traffic speed prediction is vital for intelligent transportation systems. However, most existing methods focus on costly static sensors. In contrast, utilizing GPS devices from vehicles as mobile sensors offers a cost-effective means to gather dynamic traffic data. Despite the presence of historical trajectory data, mobile sensor-based traffic prediction remains under-explored. Existing methods often treat trajectories as substitutes for static sensors, missing the full utilization of the spatial-temporal signals within the complete trajectory set. To address this, we propose TrajHGT, a novel trajectory set empowered hypergraph transformer model that captures trafficrelated spatial-temporal features through adaptive attention and fusion mechanisms in both the trajectory hypergraph space and the road graph space. Real dataset experiments demonstrate the superiority of TrajHGT.
Keywords
Traffic prediction, road sensor network, hypergraph neural network, signal processing over graphs
Discipline
Databases and Information Systems | Graphics and Human Computer Interfaces
Research Areas
Data Science and Engineering
Areas of Excellence
Digital transformation
Publication
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, April 14-19
ISBN
9798350344868
Identifier
10.1109/ICASSP48485.2024.10447016
Publisher
IEEE
City or Country
Los Alamitos, CA
Citation
ZHANG, Hanyuan; \ZHANG, Xinyu; JIANG, Qize; LI, Liang; ZHENG, Baihua; and SUN, Weiwei.
Trajectory set empowered hypergraph transformer for mobile sensor based traffic prediction. (2024). ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, April 14-19.
Available at: https://ink.library.smu.edu.sg/sis_research/9169
Copyright Owner and License
Authors
Additional URL
https://doi.org/10.1109/ICASSP48485.2024.10447016